ExperienceThinking: Constrained Hyperparameter Optimization based on Knowledge and Pruning
This addresses the need for efficient hyperparameter optimization to reduce computational costs for ML users, though it appears incremental as it builds on existing methods.
The paper tackles the problem of expensive hyperparameter evaluations in machine learning by proposing the ExperienceThinking algorithm, which quickly finds optimal configurations through search space pruning and knowledge utilization, achieving superior performance compared to three classical methods with a small number of evaluations.
Machine learning algorithms are very sensitive to the hyperparameters, and their evaluations are generally expensive. Users desperately need intelligent methods to quickly optimize hyperparameter settings according to known evaluation information, and thus reduce computational cost and promote optimization efficiency. Motivated by this, we propose ExperienceThinking algorithm to quickly find the best possible hyperparameter configuration of machine learning algorithms within a few configuration evaluations. ExperienceThinking design two novel methods, which intelligently infer optimal configurations from two aspects: search space pruning and knowledge utilization respectively. Two methods complement each other and solve the constrained hyperparameter optimization problems effectively. To demonstrate the benefit of ExperienceThinking, we compare it with 3 classical hyperparameter optimization algorithms with a small number of configuration evaluations. The experimental results present that our proposed algorithm provides superior results and achieve better performance.